Recognition of handwritten text using long short term memory (LSTM) recurrent neural network (RNN)

Abstract

Handwriting recognition is a technique which is used to produce machine readable text from a given text image. The hand written text is captured as an image from mobile. Handwritten characters are usually recognized with Optical Character Recognition (OCR) scanners. But with the large usage of mobile phones, detecting text from mobile camera has plenty of applications such as medical script processing, exam script evaluation etc. Camera image has lot of noises when compared to the OCR scanned images. Therefore, the image is pre-processed to reduce noise using image processing techniques such as binarization, thresholding and etc. The letters are segmented and extracted from an image. The features such as binary codes are extracted from the letters. The neural network classifier is built using Long Short Term Memory (LSTM) network which is trained using an already built character dataset. The neural network is used to test the input images. The output is provided as a text document with the recognized words. Since the input feed is obtained from images, the noise will be high compared to the existing system input set which uses scanned images. Noise reduction technique such as low intensity pixel removal is applied to reduce the noise from the input image for improving the efficiency.